BackgroundThe availability of sequence data of human pathogenic fungi generates opportunities to develop Bioinformatics tools and resources for vaccine development towards benefitting at-risk patients.DescriptionWe have developed a fungal adhesin predictor and an immunoinformatics database with predicted adhesins. Based on literature search and domain analysis, we prepared a positive dataset comprising adhesin protein sequences from human fungal pathogens Candida albicans, Candida glabrata, Aspergillus fumigatus, Coccidioides immitis, Coccidioides posadasii, Histoplasma capsulatum, Blastomyces dermatitidis, Pneumocystis carinii, Pneumocystis jirovecii and Paracoccidioides brasiliensis. The negative dataset consisted of proteins with high probability to function intracellularly. We have used 3945 compositional properties including frequencies of mono, doublet, triplet, and multiplets of amino acids and hydrophobic properties as input features of protein sequences to Support Vector Machine. Best classifiers were identified through an exhaustive search of 588 parameters and meeting the criteria of best Mathews Correlation Coefficient and lowest coefficient of variation among the 3 fold cross validation datasets. The "FungalRV adhesin predictor" was built on three models whose average Mathews Correlation Coefficient was in the range 0.89-0.90 and its coefficient of variation across three fold cross validation datasets in the range 1.2% - 2.74% at threshold score of 0. We obtained an overall MCC value of 0.8702 considering all 8 pathogens, namely, C. albicans, C. glabrata, A. fumigatus, B. dermatitidis, C. immitis, C. posadasii, H. capsulatum and P. brasiliensis thus showing high sensitivity and specificity at a threshold of 0.511. In case of P. brasiliensis the algorithm achieved a sensitivity of 66.67%. A total of 307 fungal adhesins and adhesin like proteins were predicted from the entire proteomes of eight human pathogenic fungal species. The immunoinformatics analysis data on these proteins were organized for easy user interface analysis. A Web interface was developed for analysis by users. The predicted adhesin sequences were processed through 18 immunoinformatics algorithms and these data have been organized into MySQL backend. A user friendly interface has been developed for experimental researchers for retrieving information from the database.ConclusionFungalRV webserver facilitating the discovery process for novel human pathogenic fungal adhesin vaccine has been developed.

Malaria caused by protozoan parasites belonging to the genus Plasmodium is a dreaded disease, second only to tuberculosis. The emergence of parasites resistant to commonly used drugs and the lack of availability of vaccines aggravates the problem. One of the preventive approaches targets adhesion of parasites to host cells and tissues. Adhesion of parasites is mediated by proteins called adhesins. Abrogation of adhesion by either immunizing the host with adhesins or inhibiting the interaction using structural analogs of host cell receptors holds the potential to develop novel preventive strategies. The availability of complete genome sequence offers new opportunities for identifying adhesin and adhesin-like proteins. Development of computational algorithms can simplify this task and accelerate experimental characterization of the predicted adhesins from complete genomes. A curated positive dataset of experimentally known adhesins from Plasmodium species was prepared by careful examination of literature reports. "Controversial" or "hypothetical" adhesins were excluded. The negative dataset consisted of proteins representing various intracellular functions including information processing, metabolism, and interface (transporters). We did not include proteins likely to be on the surface with unknown adhesin properties or which are linked even indirectly to the adhesion process in either of the training sets. A nonhomology-based approach using 420 compositional properties of amino acid dipeptide and multiplet frequencies was used to develop MAAP Web server with Support Vector Machine (SVM) model classifier as its engine for the prediction of malarial adhesins and adhesin-like proteins. The MAAP engine has six SVM classifier models identified through an exhaustive search from 728 kernel parameters set. These models displayed an efficiency (Mathews correlation coefficient) of 0.860-0.967. The final prediction P(maap) score is the maximum score attained by a given sequence in any of the six models. The results of MAAP runs on complete proteomes of Plasmodium species revealed that in Plasmodium falciparum at P(maap) scores above 0.0, we observed a sensitivity of 100% with two false positives. In P. vivax and P. yoelii an optimal threshold P(maap) score of 0.7 was optimal with very few false positives (upto 5). Several new predictions were obtained. This list includes hypothetical protein PF14_0040, interspersed repeat antigen, STEVOR, liver stage antigen, SURFIN, RIFIN, stevor (3D7-stevorT3-2), mature parasite-infected erythrocyte surface antigen or P. falciparum erythrocyte membrane protein 2, merozoite surface protein 6 in P. falciparum, circumsporozoite proteins, microneme protein-1, Vir18, Vir12-like, Vir12, Vir18-like, Vir18-related and Vir4 in P. vivax, circumsporozoite protein/thrombospondin related anonymous proteins, 28 kDa ookinete surface protein, yir1, and yir4 of P. yoelii. Among these, a few proteins identified by MAAP were matched with those identified by other groups using different experimental and theor...

Background: The sequencing of genomes of the Plasmodium species causing malaria, offers immense opportunities to aid in the development of new therapeutics and vaccine candidates through Bioinformatics tools and resources.

MicroRNAs (small ~22 nucleotide long non-coding endogenous RNAs) have recently attracted immense attention as critical regulators of gene expression in multi-cellular eukaryotes, especially in humans. Recent studies have proved that viruses also express microRNAs, which are thought to contribute to the intricate mechanisms of host-pathogen interactions. Computational predictions have greatly accelerated the discovery of microRNAs. However, most of these widely used tools are dependent on structural features and sequence conservation which limits their use in discovering novel virus expressed microRNAs and non-conserved eukaryotic microRNAs. In this work an efficient prediction method is developed based on the hypothesis that sequence and structure features which discriminate between host microRNA precursor hairpins and pseudo microRNAs are shared by viral microRNA as they depend on host machinery for the processing of microRNA precursors. The proposed method has been found to be more efficient than recently reported ab-initio methods for predicting viral microRNAs and microRNAs expressed by mammals.